.png){kind=icon}
返回
Dynamic Demand-Aware Power Grid Intelligent Pricing Algorithm Based on Deep Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3406338.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The increasing integration of renewable energy sources and the growing complexity of modern power grids demand innovative solutions for efficient energy management. This paper introduces a novel dynamic demand-aware Power Grid Intelligent Pricing (PGIP) algorithm based on Deep Reinforcement Learning (DRL). The proposed PGIP algorithm aims to optimize energy consumption and pricing in real time by leveraging the capabilities of DRL to adapt to dynamic demand patterns and evolving grid conditions. PGIP employs a sophisticated neural network architecture to model the intricate relationships between various grid parameters, user demand, and pricing strategies. Through continuous learning and adaptation, the algorithm dynamically adjusts pricing structures to incentivize demand-side flexibility while ensuring grid stability. The reinforcement learning framework enables the algorithm to discover optimal policies for pricing in response to changing environmental factors and user behaviors. We used real-world data sets to assess its performance in diverse scenarios. Results demonstrate the algorithm's ability to optimize energy consumption, reduce peak demand, and enhance overall grid efficiency. Moreover, comparisons with traditional pricing models highlight the superior adaptability and responsiveness of PGIP in addressing the challenges posed by the evolving landscape of power grids. PGIP presents a promising approach to address the dynamic nature of power grids and the increasing demand for efficient energy management.
Keyword:
Pricing
Power grids
Electricity
Heuristic algorithms
Renewable energy sources
Power system dynamics
Real-time systems
Deep reinforcement learning
Intelligent systems
dynamic demand
intelligent pricing
power grid
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
OAAI
OAAI分享收藏Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning Technique基于强化学习的电动汽车充电站Q学习负荷预测
PREAI
分享收藏Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid智能电网中纳入可再生能源的动态定价机制.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容